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 Duration 42 hours

Course Outline

Introduction to Big Data Ecosystems

  • Survey of big data technologies and system architectures
  • Comparing batch processing with real-time processing
  • Scalable data storage strategies

Advanced Data Processing with Apache Spark

  • Techniques for optimizing Spark job performance
  • Utilizing advanced transformations and actions
  • Implementing structured streaming

Machine Learning at Scale

  • Methods for distributed model training
  • Performing hyperparameter tuning on large datasets
  • Deploying models within big data environments

Deep Learning for Big Data

  • Integrating TensorFlow and PyTorch with Spark
  • Building distributed deep learning training pipelines
  • Applications in image, text, and time-series analysis

Real-Time Analytics and Data Streaming

  • Using Apache Kafka for streaming data ingestion
  • Working with stream processing frameworks
  • Monitoring and alerting mechanisms in real-time systems

Data Governance, Security, and Ethics

  • Meeting data privacy and compliance requirements
  • Implementing access control and encryption in big data systems
  • Ethical considerations in large-scale analytics

Integrating Big Data with Business Intelligence

  • Data visualization and dashboarding for big data
  • Linking big data pipelines to BI tools
  • Achieving business outcomes through advanced analytics

Summary and Next Steps

Requirements

  • A solid grasp of data analysis principles and statistical modeling concepts.
  • Proficiency in data processing tools and programming languages such as Python, R, or Scala.
  • Working knowledge of distributed computing frameworks like Hadoop or Spark.

Intended Audience

  • Data scientists looking to excel in large-scale data processing and predictive analytics.
  • Senior analysts who aim to build and implement sophisticated analytical workflows.
  • R&D specialists dedicated to developing innovative, data-driven solutions.

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